Mechanism of loss of consciousness during vascular neck restraint
Bibliographic record
Abstract
Vascular neck restraint (VNR) is a technique that police officers may employ to control combative individuals. As the mechanism of unconsciousness is not completely understood, we tested the hypothesis that VNR simply compresses the carotid arteries, thereby decreasing middle cerebral artery blood flow. Twenty-four healthy police officers (age 35 ± 4 yr) were studied. Heart rate (HR), arterial pressure, rate of change of pressure (dP/dt), and stroke volume (SV) were measured using infrared finger photoplethysmography. Bilateral mean middle cerebral artery flow velocity (MCAVmean) was measured by using transcranial Doppler ultrasound. Neck pressure was measured using flat, fluid-filled balloon transducers positioned over both carotid bifurcations. To detect ocular fixation, subjects were asked to focus on a pen that was moved from side to side. VNR was released 1-2 s after ocular fixation. Ocular fixation occurred in 16 subjects [time 9.5 ± 0.4 (SE) s]. Pressures over the right (R) and left (L) carotid arteries were 257 ± 22 and 146 ± 18 mmHg, respectively. VNR decreased MCAVmean (R 45 ± 3 to 8 ± 4 cm/s; L 53 ± 2 to 10 ± 3 cm/s) and SV (92 ± 4 to 75 ± 4 ml; P < 0.001). Mean arterial pressure (MAP), dP/dt, and HR did not change significantly. We conclude that the most important mechanism in loss of consciousness was decreased cerebral blood flow caused by carotid artery compression. The small decrease in CO (9.6 to 7.5 l/min) observed would not seem to be important as there was no change in MAP. In addition, with no significant change in HR, ventricular contractility, or MAP, the carotid sinus baroreceptor reflex appears to contribute little to the response to VNR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".